Discriminative learning for automatic staging of placental maturity via multi-layer fisher vector

Abstract

In this paper, a new method is proposed to automatically stage the placental maturity from B-mode ultrasound (US) images based on multi-layer Fisher vector (MFV) and densely sampled visual features. The proposed method first densely extracts visual features at a regular grid based on dense sampling instead of a few unreliable interest points. These features are clustered using generative Gaussian mixture model (GMM) to have soft clustering ability, and then learned discriminatively by Fisher vector (FV), which incorporates high-order statistics to enhance the staging accuracy. Differing from the previous studies, a multi-layer FV instead of a single layer FV is adopted in our method to exploit the spatial information of the features. Experimental results show that the proposed method achieves an area under the receiver of characteristics (AUC) of 96.77%, sensitivity of 98.04% and specificity of 93.75%, respectively, for staging placental maturity. Moreover, experimental results also demonstrate that the proposed MFV outperformed traditional methods for placental maturity staging.